Operational strategies for on-demand personal shopper services

Arslan, Alp; Agatz, Niels; Klapp, Mathias A.

doi:10.1016/j.trc.2021.103320

Cited by 13 publications

(7 citation statements)

References 30 publications

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“…The studies by Arslan et al (2021), Schrotenboer et al (2021) are the closest ones in terms of the setting of the IDP as these papers explore the idea of retail stores as storage facilities for expedited deliveries. Nevertheless, both of these papers, similar to the restaurant meal delivery problem, consider a setting in which customers specify the store where orders will be collected.…”

Section: Last-mile Logisticsmentioning

confidence: 99%

“…Those are tight order delivery deadlines, low order volumes, and high levels of order variability. This structure leads to inefficient delivery fleet utilization, and consequently, substantial operational expenses (Arslan 2019). In addition, there is a growing requirement for storage facilities to be in or nearby urban areas to sustain expedited delivery, which in turn increases capital investment expenses even further.…”

Section: Introductionmentioning

confidence: 99%

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Personal Shopper Systems in Last-Mile Logistics

Gaast

Arslan

2023

Service Science

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This paper explores the logistics operations of instant grocery delivery services. Therefore, we introduce the instant delivery problem (IDP) to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. In the PSS, couriers visit affiliated brick-and-mortar stores in the delivery area to pick up and purchase ordered products and then deliver them to customers. In the IOD system, couriers collect products from a single distribution center, or so-called dark store, in which the platform manages the inventory. Even though a PSS strategy is asset light because of the utilization of existing retailers in the area, maintaining a good level of on-time instant deliveries with the PSS is more complex than with IOD. This is because the PSS requires deciding which store to purchase ordered goods from, and picking and shopping at stores needs to be considered in the real-time decision process. We propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies both in real life–inspired settings and in case studies on selected urban areas show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders. We observe that the performance of the PSS is robust when varying the delivery service time frame. The case studies also suggest that the PSS becomes even more competitive in areas where the retail store density is high. History: Special Issue—Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by National Natural Science Foundation of China [Grant 72150410444] to J.P. van der Gaast. Supplemental Material: The e-companion is available at https://doi.org/10.1287/serv.2022.0310 .

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Section: Last-mile Logisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personal Shopper Systems in Last-Mile Logistics

Gaast

Arslan

2023

Service Science

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“…Notably, Arslan et al. (2021) do not consider the personal shoppers conducting their own shopping in addition to the picking and transportation tasks of assigned clients' orders, nor do they explicitly optimize picking responsibilities within the store.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To account for the potential economies of scale when a shopper can shop multiple tasks of different customer orders in one single store visit, they assume the total time spent in a store visit to be increasing and concave as a function of the number of tasks. Notably, Arslan et al (2021) do not consider the personal shoppers conducting their own shopping in addition to the picking and transportation tasks of assigned clients' orders, nor do they explicitly optimize picking responsibilities within the store.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies focus on the delivery problem only, capturing stochasticity related to both capacity (drivers) and demand (online orders). We are aware of only one paper considering both delivery and in‐store picking tasks (Arslan et al., 2021). They consider the problem of “personal shoppers” who purchase items of one or more orders, potentially from multiple stores, and then deliver them to the customers' locations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Crowdsourced order‐fulfillment policies using in‐store customers

Dayarian

Pazour

2022

Production and Operations Management

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Omni‐channel services, such as buy‐online‐pick‐up‐from‐store, transfer the in‐store logistics once completed by shoppers to retailers. To cost‐effectively meet the high demands for such pickup services, we introduce a crowdsourced order‐fulfillment policy that deploys in‐store customers to pick items for online orders while completing their own personal shopping. As opposed to existing store fulfillment policies, this new concept utilizes in‐store customers to help, not constrain, dedicated pickers. Empirical data indicate that a high percentage of surveyed in‐store shoppers would be willing to occasionally participate in such a program. In‐store customers willing to participate were observed to be heterogeneous in their efforts, with variability in how much extra time they would be willing to provide and would prefer picking tasks that had only a small deviation from their personal shopping. Motivated by these empirical results, the decision problem of how to assign picking tasks for arriving online orders with a given service commitment, to a set of arriving in‐store customers or an abundant set of dedicated pickers, was formalized to capture the uncertainty and heterogeneity of using in‐store customers for in‐store picking tasks. We propose a tractable decision‐making methodology to determine whether an order will meet both service commitment feasibility and in‐store customer availability with a probability at least equal to a target threshold. This method captures dynamic order placements and in‐store customer arrivals and stochasticity in in‐store customers' shopping baskets. Extensive computational experiments for varying operational conditions of a grocery store dynamically matching actual online orders to arriving in‐store customers helps answer open questions from practitioners. Compensating in‐store customers based on their additional efforts reduced costs of fulfillment by greater than 30%, on average, compared to a baseline that uses only dedicated pickers for store fulfillment. Using the past five shopping baskets of participating in‐store customers to estimate assignment decisions can achieve both high online order service commitments and in‐store customer availability requirements. Our results suggest that in‐store customers should be assigned smaller orders than dedicated pickers.

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Operations optimization for third-party e-grocery platforms

Wu,

Zhen,

et al. 2023

Ann Oper Res

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Operational strategies for on-demand personal shopper services

Cited by 13 publications

References 30 publications

Personal Shopper Systems in Last-Mile Logistics

Personal Shopper Systems in Last-Mile Logistics

Crowdsourced order‐fulfillment policies using in‐store customers

Operations optimization for third-party e-grocery platforms

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